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 Table of Contents  
EDITORIAL
Year : 2018  |  Volume : 3  |  Issue : 2  |  Page : 39-40

Electrocardiography clues of sudden cardiac death: From close look to deep learning


Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China

Date of Web Publication5-Feb-2019

Correspondence Address:
Dr. Minglong Chen
Division of Cardiology, The First Affiliated Hospital of Nanjing Medical University, 300# Guangzhou Road, Nanjing 210029, Jiangsu Province
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJHR.IJHR_1_19

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How to cite this article:
Cui C, Chen M. Electrocardiography clues of sudden cardiac death: From close look to deep learning. Int J Heart Rhythm 2018;3:39-40

How to cite this URL:
Cui C, Chen M. Electrocardiography clues of sudden cardiac death: From close look to deep learning. Int J Heart Rhythm [serial online] 2018 [cited 2019 Feb 17];3:39-40. Available from: http://www.ijhronline.org/text.asp?2018/3/2/39/251577


  Introduction Top


Sudden cardiac death (SCD) caused by sudden loss of heart function claims 1.5 million lives worldwide each year.[1] In general, most SCDs (~80%) are associated with ischemic heart disease,[2] while other cardiac disorders, such as valvular heart diseases, cardiomyopathies, and channelopathies, account for a smaller proportion. In this issue of International Journal of Heart Rhythm, authors summarized the risk stratification in Brugada syndrome patients and the definition of epsilon waves in arrhythmogenic right ventricular cardiomyopathy. Indeed, SCD is a complex, multifactorial process, whose mechanisms are poorly understood. Therefore, there is an urgent to stratify the high-risk patients for further treatment.

Standard 12-lead electrocardiography (ECG) is an easy and common test among the diagnostic armamentariums of clinicians. As an inspection of recording the electrical activity of the heart, this technique acts as a useful tool for assessing risks of SCD. With a close look, ECG provides clues of electrical disturbances in the heart, i.e. depolarization abnormalities, repolarization abnormalities, and inherited arrhythmia syndromes. Although in the near future, smart tools such as deep learning or machine learning can help to quickly screen or dig out the very subtle ECG changes suggesting high risk of SCD, ECG that has a close look to stratify the SCD clues remains to be a first-line, prompt, and useful tool.


  Electrocardiography Clues of Depolarization Abnormalities Top


QRS complex, which reflects ventricular depolarization, can help to evaluate SCD risk. Pathologic Q-waves, indicating previous myocardial infarction and scar, have been demonstrated to indicate an increased risk of SCD. Strauss et al.[3] calculated QRS scores to quantify scar and reported that patients with lower QRS scores have significantly fewer ventricular tachycardia (VT) or ventricular fibrillation (VF) events.

QRS duration is an independent predictor for the risk of SCD irrespective of the presence of bundle branch block (BBB) in general population. Kurl et al.[4] reported that QRS duration over 110 ms had a 2.5-fold risk for SCD compared with those with QRS duration <96 ms, after adjustment of clinical risk factors.

Fragmentation of QRS complex, excluding typical BBB pattern and incomplete right BBB, was defined as RSR' pattern or any QRS complex with more than three distinct deflections. Pei et al.[5] and others demonstrated that this ECG parameter was a marker of slow or disrupted conduction and may suggest an increasing risk of SCD.


  Electrocardiography Clues of Repolarization Abnormalities Top


QT interval prolongation may be due to the long QT syndromes or QT-prolonging drugs, defined as a corrected QT interval (QTc) above 470 ms (male) and 480 ms (female). Meanwhile, severe QT prolongation was defined as QTc >500 ms in both males and females. The QTc interval reflects ventricular repolarization; meanwhile, it also includes QRS duration. Thus, in the presence of left BBB or right BBB and Wolff–Parkinson–White syndrome, the evaluation value of QTc could be improper. In such circumstances, the measurement of corrected JT interval which excludes depolarization, is more accurate. The normal range of corrected JT interval is from 320 ms to 400 ms.

Previously, the early repolarization (ER) pattern or J-point elevation was thought to be a benign phenomenon. Haïssaguerre et al.[6] reported that ER was associated with idiopathic VF. The morphology of malignant ER pattern was described as follows: ER in inferior leads or global leads, notched terminal waves of QRS complex, J-wave amplitude of over 0.2 mV, and horizontal ST-segment elevation instead of ascending type.

Microvolt T-wave alternans, characterized as beat-to-beat fluctuation of T-wave amplitude and morphology, is an ECG marker predicting impending ventricular arrhythmias.[7] However, the spectral method requires the patients to achieve a target heart rate (105–110 bpm) as well as the use of high-resolution electrodes.

Very recently, Bundgaard et al.[8] reported a novel familial cardiac arrhythmia syndrome with widespread ST-segment depression. Patients developed SCD due to VF. Their ECGs were characterized by marked, persistent, nonischemic ST-segment depression over time. Meanwhile, the underlined mechanism remains unknown.


  Electrocardiography Clues of Inherited Arrhythmia Syndromes Top


The inherited arrhythmia syndromes include ion channelopathies and hereditary cardiomyopathies, which are uncommon causes of SCD (<5%). They usually have a clear genetic basis which interferences ion channels or leads to a cardiomyopathy. ECGs of these diseases are often characterized and are the key points for establishing the diagnosis.

Brugada syndrome

Type 1 ECG pattern, i.e., right BBB with coved ST-segment elevation over 0.2 mV in V1–V3 leads followed by a negative T wave, has been linked to an increasing risk for VT and SCD.[9] The other two types of Brugada ECG changes are nondiagnostic but warrant further investigation.

Arrhythmogenic right ventricular cardiomyopathy

A small deflection (called epsilon wave) may be observed at the terminal part of QRS complex in V1–V4 leads in ~30% of arrhythmogenic right ventricular cardiomyopathy patients.[10] They are delayed potentials indicating episodes of sustained VT, especially right ventricular outflow tract tachycardia.

Long QT syndrome

The prolonged QTc is associated with the increasing risk of VT or VF. For long QT syndrome patients, QTc is usually found to be over 500 ms. While, in short QT syndrome, the QTc is ≤ 330 ms or 360 ms when other clinical or genetic factors are present. Furthermore, epinephrine challenge has been introduced to diagnose and predict the genotype of long QT syndrome.[11]


  Conclusion Top


The 12-lead ECG is the most common test. Interpreting the ECG, however, needs close look and deep learning. Meanwhile, no single ECG clue has been proved to precisely stratify patients for SCD. These ECG variables should be used to calculate SCD risk in a cumulative manner, together with other clinical examinations, such as ejection fraction, electrophysiological testing, and gene screening. Moreover, new statistical approaches using reclassification along with the development of remote monitoring for dynamic observation enhance ECG-based SCD risk prediction with higher sensitivity and specificity.[12]

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest

 
  References Top

1.
Writing Group Members, Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, et al. Heart disease and stroke statistics-2016 update: A report from the American Heart Association. Circulation 2016;133:e38-60.  Back to cited text no. 1
    
2.
Feng XF, Hai JJ, Ma Y, Wang ZQ, Tse HF. Sudden cardiac death in mainland China. Circ Arrhythm Electrophysiol 2018;11:e006684.  Back to cited text no. 2
    
3.
Strauss DG, Poole JE, Wagner GS, Selvester RH, Miller JM, Anderson J, et al. An ECG index of myocardial scar enhances prediction of defibrillator shocks: An analysis of the sudden cardiac death in heart failure trial. Heart Rhythm 2011;8:38-45.  Back to cited text no. 3
    
4.
Kurl S, Mäkikallio TH, Rautaharju P, Kiviniemi V, Laukkanen JA. Duration of QRS complex in resting electrocardiogram is a predictor of sudden cardiac death in men. Circulation 2012;125:2588-94.  Back to cited text no. 4
    
5.
Pei J, Li N, Gao Y, Wang Z, Li X, Zhang Y, et al. The J wave and fragmented QRS complexes in inferior leads associated with sudden cardiac death in patients with chronic heart failure. Europace 2012;14:1180-7.  Back to cited text no. 5
    
6.
Haïssaguerre M, Derval N, Sacher F, Jesel L, Deisenhofer I, de Roy L, et al. Sudden cardiac arrest associated with early repolarization. N Engl J Med 2008;358:2016-23.  Back to cited text no. 6
    
7.
Lewek J, Ptaszynski P, Klingenheben T, Cygankiewicz I. The clinical value of T-wave alternans derived from holter monitoring. Europace 2017;19:529-34.  Back to cited text no. 7
    
8.
Bundgaard H, Jøns C, Lodder EM, Izarzugaza JM, Romero Herrera JA, Pehrson S, et al. A novel familial cardiac arrhythmia syndrome with widespread ST-segment depression. N Engl J Med 2018;379:1780-1.  Back to cited text no. 8
    
9.
Adler A, Rosso R, Chorin E, Havakuk O, Antzelevitch C, Viskin S, et al. Risk stratification in brugada syndrome: Clinical characteristics, electrocardiographic parameters, and auxiliary testing. Heart Rhythm 2016;13:299-310.  Back to cited text no. 9
    
10.
Nasir K, Bomma C, Tandri H, Roguin A, Dalal D, Prakasa K, et al. Electrocardiographic features of arrhythmogenic right ventricular dysplasia/cardiomyopathy according to disease severity: A need to broaden diagnostic criteria. Circulation 2004;110:1527-34.  Back to cited text no. 10
    
11.
Schimpf R, Veltmann C, Wolpert C, Borggrefe M. Arrhythmogenic hereditary syndromes: Brugada syndrome, long QT syndrome, short QT syndrome and CPVT. Minerva Cardioangiol 2010;58:623-36.  Back to cited text no. 11
    
12.
Lambiase PD, Young WJ. Sudden cardiac death risk prediction – As easy as ECG? Int J Cardiol 2019;276:152-3.  Back to cited text no. 12
    




 

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